The prediction of optical flow for occluded points is still a difficult
problem that has not yet been solved. Recent methods use self-attention to find
relevant non-occluded points as references for estimating the optical flow of
occluded points based on the assumption of self-similarity. However, they rely
on visual features of a single image and weak constraints, which are not
sufficient to constrain the trained network to focus on erroneous and weakly
relevant reference points. We make full use of online occlusion recognition
information to construct occlusion extended visual features and two strong
constraints, allowing the network to learn to focus only on the most relevant
references without requiring occlusion ground truth to participate in the
training of the network. Our method adds very few network parameters to the
original framework, making it very lightweight. Extensive experiments show that
our model has the greatest cross-dataset generalization. Our method achieves
much greater error reduction, 18.6%, 16.2%, and 20.1% for all points,
non-occluded points, and occluded points respectively from the state-of-the-art
GMA-base method, MATCHFlow(GMA), on Sintel Albedo pass. Furthermore, our model
achieves state-of-the-art performance on the Sintel bench-marks, ranking \#1
among all published methods on Sintel clean pass. The code will be open-source.

通过利用在线遮挡识别信息构建扩展可视特征和两个强约束条件，该研究实现了光流预测中对被遮挡点的精确定位，取得了当前领先方法的显著误差减小，对所有点、未被遮挡的点和被遮挡的点分别比 GMA-base 方法 (MATCHFlow (GMA)) 分别减小了 18.6%、16.2% 和 20.1%，在 Sintel clean pass 上位列所有已发布方法之首，代码将会开源。